EAI Endorsed Transactions on e-Learning 2023-03-01T14:18:20+00:00 EAI Publications Department Open Journal Systems <p>EAI Endorsed Transactions on e-Learning is open access, a peer-reviewed scholarly journal focused on topics belonging to the variegated and engaging e-Learning landscape, ranging from various types of distance learning (e.g., online, mobile, cloud, hybrid) to virtual laboratory environments supported by sound pedagogies, cutting-edge technologies and much more. The journal publishes research, review, commentaries, editorials, technical articles, and short communications with a triannual frequency. Authors are not charged for article submission and processing.</p> Analyzing the Effects of Eco-Spirituality on Organizational Commitment and Employee Engagement Among Female Academics in Higher Education 2022-12-31T12:18:03+00:00 Shaan Gulhar Anshu Singh Priyanka Agarwal <p>This study investigated the connections between eco-spirituality, organizational commitment, and employee engagement by female academics within higher education institutions. The results of this study indicate that eco-spirituality has an effect on organizational commitment, and organizational commitment has an effect on employee engagement. Both of these relationships were found to be significant. In addition, this research's findings indicate a direct and indirect relationship between employee engagement and eco-spirituality. Even though this relationship has never been investigated in any of the previous studies, the findings of this research show that there is such a relationship. An employee engagement study, an organizational commitment study, and an employee spirituality study were all conducted using regression analysis. We also considered the correlation between the two data sets when analyzing the connection between the dependent and the independent variables. An examination of the construct item's dependability was carried out.</p> 2023-03-30T00:00:00+00:00 Copyright (c) 2023 EAI Endorsed Transactions on e-Learning Effective Tamil Character Recognition Using Supervised Machine Learning Algorithms 2023-02-08T09:33:59+00:00 Dr. S. Suriya S. Nivetha P. Pavithran Ajay Venkat S. Sashwath K. G. Elakkiya G. <p class="ICST-abstracttext" style="margin-right: 33.2pt;"><span lang="EN-GB">Computational linguistics is the branch of linguistics in which the techniques of computer science are applied to the analysis and synthesis of language and speech. The main goals of computational linguistics include: Text-to- speech conversion, Speech-to-text conversion and Translating from one language to another. A part of Computational Linguistics is the Character recognition. Character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. Character recognition methodology mainly focuses on recognizing the characters irrespective of the difficulties that arises due to the variations in writing style. The aim of this project is to perform character recognition for of one of the complex structures of south Indian language ‘Tamil’ using a supervised algorithm that increases the accuracy of recognition. The novelty of this system is that it recognizes the characters of the Predominant Tamil Language. The proposed approach is capable of recognizing text where the traditional character recognition systems fails, notably in the presence of blur, low contrast, low resolution, high image noise, and other distortions. This system uses Convolutional Neural Network Algorithm that are able to exact the local features more accurately as they restrict the receptive fields of the hidden layers to be local. Convolutional Neural Networks are a great kind of multi-layer neural networks that uses back-propagation algorithm. Convolutional Neural Networks are used to recognize visual patterns directly from pixel images with minimal preprocessing. This trained network is used for recognition and classification. The results show that the proposed system yields good recognition rates.</span></p> 2023-02-08T00:00:00+00:00 Copyright (c) 2023 EAI Endorsed Transactions on e-Learning The Teaching Physical Exercise with Music – Pedometric Evaluation 2023-02-24T07:39:08+00:00 W.Vinu W G. Vinod Kumar S. Sivachandiran <p class="ICST-abstracttext"><span lang="EN-GB">In everyday life and culture, music can be encountered and experienced in a variety of forms, and it plays a role in mood swings. Numerous studies have shown that listening to music while exercising increases both the amount of time spent exercising as well as the interest level in the activity. It is hypothesised that instructing pupils in physical activities through the medium of music would have a beneficial effect on them. Fifty-five students from the Faculty of Physical Education were chosen to serve as study subjects in order to investigate the impact that music has on the process of learning and doing the activity. This study was carried out over the course of two days, and the data was gathered by counting the number of footsteps that participants made throughout a period of 20 minutes of instruction with or without music. The exercises were demonstrated to the participants over the course of two days; on the first day, they were demonstrated with music, and on the second day, they were demonstrated without music. According to the findings of this study, there is a discernible contrast between instructing activities with and without the use of music. The topic revealed a tremendous amount of interest and vitality when it was practised with music. The pedometric measure improved with musical training, and males did much better than girls in this regard. </span></p> 2023-04-06T00:00:00+00:00 Copyright (c) 2023 EAI Endorsed Transactions on e-Learning Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis 2023-03-01T14:18:20+00:00 Wenhao Tang <p class="ICST-abstracttext"><span lang="EN-GB">As the widespread transmission of COVID-19 has continued to influence human health since late 2019, more intersections between artificial intelligence and the medical field have arisen. For CT images, manual differentiation between COVID-19-infected and healthy control images is not as effective and fast as AI. This study performed experiments on a dataset containing 640 samples, 320 of which were COVID-19-infected, and the rest were healthy controls. This experiment combines the gray-level co-occurrence matrix (GLCM) and random vector function link (RVFL). The role of GLCM and RVFL is to extract image features and classify images, respectively. The experimental results of my proposed GLCM-RVFL model are validated using K-fold cross-validation, and the indicators are 78.81±1.75%, 77.08±0.68%, 77.46±0.73%, 54.22±1.35%, and 77.48±0.74% for sensitivity, accuracy, F1-score, MCC, and FMI, respectively, which also confirms that the proposed model performs well on the COVID-19 detection task. After comparing with six state-of-the-art COVID-19 detection, I ensured that my model achieved higher performance.</span></p> 2023-06-01T00:00:00+00:00 Copyright (c) 2023 EAI Endorsed Transactions on e-Learning